Complementing the genome with its...
Transcript of Complementing the genome with its...
International Agency for Research on CancerLyon, France
Complementing the genome with its “exposome”
Dr Christopher P Wild PhD
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IARC, GLOBOCAN 2002IARC, GLOBOCAN 2002
The Growing Global Cancer Burden
In 2008 : 12.4 million new cases; 7.6 million deaths
Environment and cancer prevention
• One third of cancers are preventable – the most cost-effective response (Action against Cancer: European Partnership)
• But the majority (~90%) of cancers have an environmental cause, so the potential for prevention is much higher; “aetiology gap”
• Increased research on causes of cancer e.g.• Diet and metabolism• Environmental chemicals
Importance of environmental exposure assessment
• Most major common diseases have an environmental aetiology
• Currently exposure measurement is problematic in many areas, leading to misclassification
• Large prospective cohort studies (e.g. UK Biobank) are predicated on the availability of accurate exposure assessment
• Exposure biomarkers can contribute to several areas in addition to elucidating disease aetiology
Complementing the genome with an “exposome”: the outstanding challenge of environmental
exposure measurement in molecular epidemiology
• Wild CP (2005) Cancer Epidemiology, Biomarkers and Prevention, 14: 1847-1850.• Wild CP (2009) Mutagenesis 24: 117-125.
Uca Pugnax, the male Fiddler Crab
What is the “exposome”?
• “At its most complete, the exposomeencompasses life-course environmental exposures (including lifestyle factors) from the prenatal period onwards”
• A comprehensive measurement of all exposure events (exogenous and endogenous) from conception to death
Challenges in characterising the “exposome”
• Scale and complexity: characterisation of life-course environmental exposures, including lifestyle, nutrition, occupation etc., as well as endogenous events at different target sites within the body
• Dynamic: Unlike the genome, the “exposome”changes over time – possibility of critical windows of exposure e.g. in early life
• However, even partial characterisation can bring major benefits
Aim and Approaches
Exposure x Time » “exposome”• Tools
• Laboratory technology• Cohorts/biobanks
• Skills• Inter-disciplinary• Epidemiology; biostatistics; laboratory sciences;
bioinformatics• Co-operation
• International co-ordination (funding and science)• Integration with other initiatives
Advances in exposure assessment
• Biomarkers• Geographic information systems• Personal and environmental
monitoring• Increasingly sophisticated
questionnaires
Exposure biomarkers – what do they promise?
• Defining etiology• Improved exposure assessment –
reduced misclassification• Identifying susceptible individuals or sub-
groups – heterogeneous response to exposure
• Contributing to biological plausibility
Exposure biomarkers – what do they promise?
• Evaluating Interventions• Primary and secondary prevention• Bio-monitoring e.g. occupational setting
• Hazard and Risk Assessment• Mechanistic data (e.g. IARC Monographs)• Extrapolation from animal to human
(reducing uncertainties)• Pharmacokinetic-based models
Interaction between Interaction between HBV infection anHBV infection and d aflatoxinsaflatoxins in in hepatocellularhepatocellular carcinomacarcinoma
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HBVHBV(HBsAg)
AflatoxinsAflatoxins(urinary biomarkers)
HBV and AflatoxinsHBV and Aflatoxins
adapted from Qian et al, CEBP 1994, following Ross et al., Lancet 1992
nonenone
Validation and application
• A plea for validation – difficult to find support for , but essential for progress
• An integral part of method development should be the consideration of throughput, cost and applicability to biobank samples
Deoxynivalenol(“vomitoxin”)
• Produced by Fusaria spp; common contaminant of cereals • Vomiting, feed refusal, weight loss, immuno-modulation in
animals and induces IgA nephropathy in mice• Alters cell signalling and cytokine expression (e.g. MAPK)• Linked to GI poisoning in China and India • Urinary biomarker applied to the UK National Diet and
Nutrition Survey samples
Group Cereal Intake g/day (range)
DON µg/dayMean (range)
Low 107 (88-125) 6.6 (5.8-7.6)
Medium 179 (162-195) 9.6 (8.4-11.0)
High 300 (276-325) 13.1 (11.5-15.1)
DON exposure in relation to cereal consumption
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Data are adjusted for sex, age and BMI; p for trend <0.001, adjusted R2 =0.182.
DON was detected in 296/300 (98.7%) of the urines
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Intervention study to reduce DON exposure by avoidance of cereals
R2 = 0.74
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Validation requirements
• Analytical performance• Dose-response relationship between
exposure and biomarker• Intra-individual variation over time
Biomarker validation
• RecommendationØPriority to funding biomarker
development and validation under a structured programme of priority exposures
Complementary emphasis in exposure biomarkers
• First generation exposure biomarkers tended to focus on a classical mutagen – carcinogen model of carcinogenesis (metabolites, adducts, chromosomal alterations, somatic mutations)
• Further advances are expected with a new generation of tools to measure these endpoints (e.g. advances in mass spectrometry, lab-on-a-chip)
A new generation of tools for exposure assessment – emergence from new knowledge of mechanisms
• Epigenetic changes (promoter methylation, histone acetylation, microRNA)
• Altered gene, protein or metabolite levels (“omics” technologies) - pathways
Diverse exposures, common pathways
DietInfections
Chemicals Physical activity StressLifestyle Obesity Behaviour
Epigenetic effects
Cancer
Epigenetic biomarkers – applicability to
population studies Quantitative analysis of DNA methylationafter whole bisulfitomeamplification of a minute amount of DNA from body fluids(Vaissiere et al., Epigenetics, 2009)
Quantitative profiles of DNA methylation in plasma DNA samples
PLASMA(qMAMBA)
WBC(Pyroseq.)
MLH1CDKN2ARASSF1ALINE1 MTHFR
DNA methylation levels of 4 cancer-associated genes and repetitive elements (LINE1) in plasma DNA samples (as analyzed by qMAMBA, upper panels) and corresponding white blood cells (as analyzed by pyrosequencing, lower panels) of different cancer patients (from EPIC cohort)
Epigenetic biomarkers – applicability to population studies
• Detection of stable miRNAs in plasma and serum – differences by disease status (Mitchell et al., PNAS 105: 10513, 2008; Chen et al., Cell Res., 18: 997, 2008)
• Cell and tissue specific expression• Stable in biological fluids such as plasma and serum• PCR based assays available• Profiling a small number may provide discrimination• Genetic variations in miRNA processing genes and in
miRNA binding sites may confer genetic susceptibility
• Functional information is vital
Can “omics” help improve exposure assessment?
• Do specific exposures, or categories of exposure, alter the expression of specific groups of genes, proteins or metabolites (“exposure fingerprint”)?
• How do such alterations relate to dose?• How stable are the alterations over time?• How do potential confounding factors affect the
association between exposure and “omics”biomarkers
Transcriptomics and exposure assessment(see Wild CP , Mutagenesis 24: 117-125, 2009)
• Smoking – Lampe et al., CEBP, 13: 445-453, 2004; van Leeuwen et al., Carcinogenesis, 28: 691-697, 2007
• Benzene - Forrest et al., EHP 113: 801, 2005
• Arsenic – Fry et al., PLoS Genet., 3: 2180-2189, 2007; Wu et al., 111: 1429-1438, 2003
• Metal fumes – Wang et al., Env. Health Persp., 113: 233-241, 2005
• Air pollution – van Leeuwen et al., Mutat. Res., 600: 12-22, 2006
Metabonomics and population studies
• Connects molecular events to those at the macro level
• Applicable to blood and urine samples• LC-mass spectrometry methodology
affordable and of requisite throughput • Demonstrated applicability to studies of diet
(Solanky et al., Anal. Biochem., 323: 197-204, 2003; Holmes et al., Nature, 453: 396-400, 2008)
Problems in comparisons of “omics”data in poorly designed studies
• Unmeasured confounding by lack of information on age, sex and other exposures
• Bias through differences in sample processing• Selection bias through sampling procedures• High costs leading to one-off or small-scale
studies
See Potter JD Trends in Genetics, 19: 690-695, 2003
Next-Generation DNA Sequencing and molecular epidemiology
(Pleasance et al., Nature Jan 14, 2010)
• Massively parallel sequencing techniques promise the capacity to paint a genome-wide portrait of mutation in human cancer
• Tumour (small cell lung) and normal cell lines derived from same individual
• 22,910 somatically acquired substitutions consistent with tobacco exposure
• The smoking history of the patient is not recorded
Next-generation biomarkers and epidemiology
• RecommendationØPriority on exploring how next-
generation biomarkers reflect exposure and reveal relevant disease mechanisms
Early life exposure and cancer risk
• Observational studies linking early life exposures (or intergenerational effects) to disease later in life
• Foetal programming; epigenetic remodelling; adaptive response
• Vulnerability of children to environmental exposures (Wild and Kleinjans, Cancer Epi. Bio. Prev., 2003)
Exposure Disease
Temporal application of exposure biomarkers in cancer epidemiology
Adult cohortCase-control
study
Timing ofexposuremeasurement
PerinatalChildhood
Adolescence Adult
Birth cohort
Adolescent cohort
Longitudinal study of aflatoxinexposure and child growth in Benin
Subjects: 200 children, aged 16-37 months from four villages, two high, two low aflatoxin exposure
Time: February May/June October
Survey: 1 2 3
Serum AF-alb: X X X
Anthropometry: X X X
Questionnaire: X X X
4.2 (3.9,4.6)4.1 (3.8,4.5)**upper quartile4.8 (4.4,5.2)4.1 (3.8,4.5)**mid-upper quartile5.3 (4.8,5.9)4.4 (4.1,4.7)**mid-lower quartile5.9 (5.2,6.6)4.9 (4.5,5.3)*,clower quartile
Mean AF-alb over 8 monthsHeight increase (cm)
Unadjusted Adjusteda
AflatoxinExposure Group
Longitudinal Study of AflatoxinExposure and Child Growth in Benin
200 children, aged 16-37 months followed over 8 monthsaAdjusted for age, height, weaning status, mothers SES and village. cData labelled * are significantly different to **.
Gong et al., Environ. Health Perspec. (2004) 112, 1334-1338
Activation of inflammation/NF-κB signalling in infants born to arsenic-exposed mothersFry et al., PLoS Genetics, 3: 2180-2189, 2007
• 32 pregnant women in Thailand in high and low areas of arsenic exposure
• Toenail analysis of arsenic; cord blood for microarray gene expression
• Expression signatures highly predictive of prenatal arsenic exposure; genes related to stress, inflammation, metal exposure and apoptosis
Gene expression in the placentas of cigarette-smoking mothers
Huuskonen et al., Clin. Pharmacol. Ther ., 2008
Early life exposure and cancer risk
• Mechanism-based biomarkers to relate exposure to disease – a necessity?
Cohorts to cover life course
• Rich resource of cohorts (with biobanks) internationally spanning exposures from in utero to adult life
• Many cohorts exist but struggle for long-term support
• New cohorts may be envisaged to fill gaps• in age range• In geographic distribution (most cohorts in
high resource countries)
Cohort studies to cover life-course
• Recommendation:ØInfrastructure support to a network of
recognized “lifecourse cohorts” which include biological banksØConsider the possibility of support for
repeat measurementsØComprehensive review of current
networks and initiatives
More than exposure assessment…. biomarkers and biological plausibility
• Proof exposure, biological evidence• Demonstration of a plausible mechanism
Demonstration of exposure –environmental tobacco smoke
Nicotine/CotinineUrinary TSNA4-ABP-HbUrinary mutagenicity
Demonstration of exposure and plausibility of association with disease
Anderson et al., JNCI, 93: 378-381, 2001
Red Meat and Colon Cancer Risk: biomarkers and biological plausibility
• Red and processed meat is associated with increased colorectal cancer (CRC); one hypothesis is that this is due to heterocyclic amines (HCA)
• However, white meat contains HCA but is not associated with CRC risk
• Studies of the N-acetyl gene required for HCA activation and CRC are equivocal
• Red but not white meat stimulates endogenous intestinal N-nitrosation in humans
Red Meat and Colon Cancer Risk: biomarkers and biological plausibility
• Volunteers in metabolic suite
• Fed high (420g) red meat, vegetarian and high red meat, high-fibre diets for 15 days in randomized cross-over trial
• Tested whether total faecal N-nitroso compounds and O6-carboxymethylguanine adducts in colon DNA were associated with red meat diet
Lewin et al., Cancer Res 2006
Red Meat and Colon Cancer Risk: biomarkers and biological plausibility
More than exposure assessment…. biomarkers and intervention studies
• Proof of concept for modifying exposure-disease relationship (e.g. anti-oxidants, induction of detoxification enzymes, avoidance of exposure)
• Surrogate (earlier) outcome
Sept/Oct Dec/Jan Feb/Mar
Survey 1 Survey 2 Survey 3
20 Villages (10 intervention, 10 control), 30 subjects per village
Blood sample collection Groundnut sample collection
IntermediateSurvey 1
IntermediateSurvey 2
Biomarkers and intervention studies –aflatoxin in subsistence farms in Guinea
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Turner et al., (2005) The Lancet, 365, 1950-1956
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Turner et al., (2005) The Lancet, 365, 1950-1956
A plea for two-way translation
Basic ClinicalPopulation
MechanismsCausesInterventions
Early detectionTreatment
“Common soil” of mechanistic research
Summary – 1
Ø Invest in development and validation of exposure biomarkers to complement genetic analysis in epidemiological studies
Ø Encourage application of new methodologies (e.g. “omics”) and knowledge of mechanisms (e.g. epigenetics) to population-based research
Ø Consider infrastructure support to key prospective cohort studies in relation to coverage of lifecourse and geography
Summary – 2
Ø Prioritize studies of biological plausibility in establishing aetiology, particularly in cases of modest risk elevation such as diet and cancer
Ø Explore the integration of biomarkers into proof-of-principle intervention studies
Ø Train a new generation of multi-lingual researchers able to operate in an inter-disciplinary environment
Acknowledgements
• To many colleagues at IARC, University of Leeds and internationally
• To NIEHS, USA Grant No. ES06052 (PI: Dr John Groopman, Johns Hopkins Univ.)